1. Field of the Invention
The present invention relates to a control device for a plant, and a control device for a thermal power plant adapted to control a thermal power plant equipped with a boiler.
Further, the present invention relates to a gas concentration estimation device and a gas concentration estimation method of a coal-burning boiler provided to the thermal power plant, and in particular to a gas concentration estimation device and a gas concentration estimation method of a coal-burning boiler adapted to estimate the concentrations of CO and NOx as gas components included in the exhaust gas emitted from the coal-burning boiler.
2. Description of the Related Art
In general, a control device for controlling a plant processes a measurement signal obtained from the plant as a controlled object, and calculates a manipulation signal to be applied to the controlled object to output as a control signal.
The control device for a plant is provided with an algorithm for calculating the manipulation signal so that the measurement signal of the plant satisfies the target value.
As a control algorithm used for the control of a plant, there is cited a proportional-and-integral (PI) control algorithm.
In the PI control, the manipulation signal to be applied to the controlled object is obtained by adding a value obtained by temporally integrating deviation of the measurement value of the plant from the target value thereof to a value obtained by multiplying the deviation by a proportional gain.
Since the control algorithm using the PI control can describe the input-output relationship with a block diagram and so on, the cause-and-effect relationship can easily be understood. Further, the PI control is a stable and safe control algorithm in plant control, and therefore, has a lot of records of application to actual equipment.
However, in the case in which the plant is operated in an unexpected condition such as a change in the operation mode of the plant or a change in the environment, some operation such as modification of the control logic is required in some cases.
Incidentally, adaptive control for automatically correcting and modifying the control method in accordance with a change in the operation mode of the plant or a change in the environment is also available.
As a plant control method using a learning algorithm as one of the adaptive control methods, there can be cited a technology described in JP-A-2000-35956, for example.
In the plant control method using the learning algorithm as the technology described in JP-A-2000-35956, the control device is provided with a model for estimating the characteristic of the controlled object, and a learning section for learning a method of generating a model input with which a model output achieves the target value thereof.
Further, as a learning algorithm, a document “Reinforcement Learning” (joint translator: Sadayoshi Mikami and Masaaki Minagawa, Morikita Publishing Co., Ltd., published: Dec. 20, 2000, paragraph 142-172 and 247-253) describes a method of providing a positive evaluation value when the measurement signal achieves the operation target value, and learning a method of generating a manipulation signal using an algorithm such as Actor-Critic, Q-learning, or Real-Time Dynamic Programming based on the evaluation value.
Further, in thermal power plans equipped with a coal-burning boiler, which uses coal as fuel, the concentrations of CO and NOx, which are environmental pollutants included in the exhaust gas emitted from the coal-burning boiler, are required to be suppressed to be lower than the respective regulation values.
The amounts of production of CO and NOx included in the exhaust gas of the coal-burning boiler correlate inversely with each other, and when burning coal in the coal-burning boiler, if the air (oxygen) for combustion is excessively supplied, the amount of production of NOx increases, and if the air supply is insufficient to the contrary, the amount of production of CO increases.
In recent coal-burning boilers, in order for reducing the amounts of production of both of CO and NOx and at the same time improving the combustion efficiency of the coal-burning boilers, there is adopted a two-stage combustion system in which the combustion air is fed in the coal-burning boiler in stages.
In the combustion control by the two-stage combustion system, an adjustment of the amount of combustion air supplied to the coal-burning boiler, selection of a combustion pattern of a burner provided to the coal-burning boiler, and so on are performed to create the optimum combustion condition of the coal-burning boiler.
Further, the adjustment (e.g., an adjustment of a control gain, planning of the burner combustion pattern) for optimizing the combustion control has previously been executed off-line.
It should be noted that the combustion conditions thus adjusted previously are optimized with respect to a typical operation mode, and nothing more than a rough operation plan.
Further, since the characteristic of the coal-burning boiler as a plant is varied by age deterioration, the optimum combustion conditions at the time when the operation of the coal-burning boiler starts are gradually shifted from the actual optimum combustion conditions across the ages.
On the other hand, it is required from an economic viewpoint to perform optimization (to maximizing the combustion efficiency while suppressing the concentrations of CO and NOx within allowable ranges) of the operation of coal-burning boilers in accordance with ever-changing operation conditions such as load requirement values and age deterioration.
In order for realizing the optimization of the operation of coal-burning boilers, it is required that the variation in the concentrations of CO and NOx in the exhaust gas responsive to a change in a control demand based on the present operation conditions can be simulated on-line.
Specifically, there is required a function of evaluating the combustion efficiency of the coal-burning boiler and amounts of emission of environmental-load materials in the case in which the control demand is changed with respect to the present operation conditions of the coal-burning boiler obtained from measurement data, and searching the optimum control point in view of the both points.
There are several methods for estimating the concentrations of CO and NOx in the exhaust gas emitted from coal-burning boilers, and a method of modeling the relationship between each of the operation conditions and variation trend of the gas concentrations using the data of actual equipment based on a learning algorithm such as a neural network is available.
For example, JP-A-2007-264796 discloses, with respect to creation of a continuous model for simulating the characteristic of a plant used for controlling a boiler, a control method of creating the continuous model based on process data of the boiler, creating the continuous model again using mechanically analyzed process data and operation data of the actual equipment, performing reinforcement learning using the continuous model thus created again to control the boiler, thereby reducing the environmental-load materials in the exhaust gas. Further, it is suggested that a neural network is used for creating the continuous model.
In the case of such a modeling method, by providing actual equipment data, estimation models of the CO concentration and the NOx concentration corresponding to the characteristic of the actual equipment can easily be created. In other words, since even after the operation of the coal-burning boiler is started, the estimation models suitable for the state can be created by using the data of the actual equipment in operation, such a modeling method is used frequently.
In applying the plant control technology using the learning algorithm described in JP-A-2000-35956 to the plant control, if the model for predicting the characteristic of the plant as a controlled object and the characteristic of the actual plant do not match each other, there is caused a difference between the predicted value of the model and the actual measurement value of the plant.
Therefore, even if the manipulation conditions are optimum in the predicted value of the model, the manipulation conditions are not optimum for the actual plant, and consequently, the plant cannot properly controlled with these manipulation conditions.
As a plant control method capable of avoiding the phenomenon described above, there can be cited a method of correcting the model using the measurement value of the actual plant so as to match the actual plant characteristic and the characteristic of the model with each other.
However, according to the method described above, it requires a long period of time to accumulate the measurement data of the actual plant necessary for correcting the model, moreover, the expected control performance is not exerted during the period for accumulating the data.
The technology for appropriately coping with the case in which the characteristics of model and the actual plant do not match each other is not at all described in JP-A-2000-35956.
An object of the present invention as an embodiment is to provide a control device for a plant and a control device for a thermal power plant each capable of preferably maintaining the control characteristic of the plant even in the case in which the characteristic of the model for predicting the characteristic of the plant as a controlled object is different from the characteristic of the actual plant.
The actual equipment data as the measurement value of the coal-burning boiler includes transitional states in changing the operation conditions such as the output. In this case, correlation between the measurement values corresponds to a temporary state, and therefore, shows a state different from the correlation after the state of the plant is settled with elapse of time.
In the case in which it is attempted to model the dynamic characteristic of the plant using the neural network suggested in JP-A-2007-264796, it is effective to perform the learning of the neural network using such actual equipment data.
However, in the case in which it is attempted to learn the static characteristic of the plant, an error is caused in modeling by using such actual equipment data including the transitional state to the learning. Further, in general, measurement values of the plant include a measurement error.
For example, although the temperature or the like can be measured with high accuracy, the flow rate or the like is apt to include a measurement error. Further, the age deterioration in sensors also cause a measurement error. Therefore, there are mixed data with high accuracy and data with low accuracy including a large error in the actual equipment data.
If the learning of the neural network is performed using the actual equipment data including such data with low accuracy mixed thereto, the model of the neural network thus constructed also has low accuracy.
As a result, in the case in which the estimation model of the concentrations of CO and NOx included in the exhaust gas emitted from the coal-burning boiler as a controlled object by learning the trend of the actual equipment data using the actual equipment data including the data with low accuracy mixed thereto, there arises a problem that the estimation accuracy of the model thus constructed becomes lowered.
An object of the present invention as another embodiment is to provide a gas concentration estimation device of a coal-burning boiler and a gas concentration estimation method each suppressing an estimation error of a neural-network model caused by a measurement error included in actual equipment data in the case in which the variation in the concentration of CO or the concentration of NOx in the exhaust gas is simulated using a neural network in combustion control of the coal-burning boiler, thereby making it possible to estimate the gas concentration with high accuracy.